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5 Tips on How to Visualize Data for Insightful Solutions

The human brain processes visual cues faster than written ones. According to Visme (2022), 90% of the information communicated to our brain is visual. This is why utilizing visual aids in problem-solving helps people to find solutions faster. Visual problem-solving refers to the process of finding solutions to problems with the use of visual aids like flowcharts, diagrams, maps, infographics, etc. In simple terms, you solve problems using visual aids. This method helps people understand the situation better as visual tools showcase how elements relate to each other more clearly. 

The significance of visual data representation lies in its ability to make people see and understand data easier and better. With visual elements, complex data becomes easier to grasp so that everyone can understand them no matter their level of expertise. Visual data representation also helps businesses to gather insight from vast amounts of data. It simplifies data representation and allows it to be used efficiently by entrepreneurs.

The Role of Visual Data Representation in Problem-Solving

1. Communicating Complex Information

Visual data representation can transform complex ideas into simple information, making them easier to communicate to other people. It can bridge gaps in the communication process, especially when working with people from different backgrounds and levels of expertise. Visual data representation makes it faster to disseminate concepts, findings, and solutions to others, enabling efficient collaboration within teams.

2. Enhancing Understanding and Analysis

Visual data representation makes individuals understand and analyze complicated problems more easily. Visual tools to convey data and abstract ideas can make complex concepts easier to grasp, particularly when handling and analyzing essential aspects and relationships within a problem. It aids in breaking down difficult problems transforming them into smaller components that are easier to manage and understand.

3. Enabling Informed Decision-Making

Visual data representation enables informed decision-making. Visualization tools help people evaluate and compare different concepts or solutions, allowing for more informed decision-making. Its decomposition of complex problems into manageable components allows for a more structured problem-solving process. Furthermore, it reduces the probability of having cognitive biases that may affect choices.

Types of Visual Data Representation

1. Graphs and Charts

a. Line Graphs

A line graph is a chart that displays a line joining several points or a line that shows the relationship between two points. It is commonly used to display data or information that changes over time. It consists of the horizontal “x-axis” and vertex “y-axis”.

b. Bar Charts

A bar chart is a graph that displays categorical data using horizontal or vertical bars with heights or lengths in proportion to the values they represent. It is commonly used to present categorical and discrete or continuous variables grouped in class intervals. 

c. Pie Charts

A pie chart is a circular graph that is divided into slices or sectors to present numerical proportions. The length or size of each slice or section is proportional to the data it is meant to represent.

2. Infographics

a. Visual Storytelling Through Data

An infographic is a visual image used to represent information, data, or knowledge. It tells a story visually through data, enabling a more compelling way to make people learn and understand information. An infographic with good visual storytelling includes great content, typography, colors, symbols, grids, hierarchies, icons, white space, and more. You can create and enhance your infographics by using a background remover to efficiently remove unnecessary backgrounds from the visual elements in your infographic so people can focus more on the visual storytelling and not get distracted. 

b. Combining Text and Graphics for Clarity

Infographics provide clarity by combining text and graphics. It helps in summarizing complex situations, managing information, heightening engagement, and increasing comprehension. By having graphics and text together, infographics give a clearer view of the data or information presented so different individuals can easily understand them.

3. Heatmaps and Treemaps

a. Identifying Patterns in Data Density

A heatmap is a 2D data visualization tool that represents data using colors to show density or magnitude. Heatmaps are commonly used in marketing, website analytics, and consumer behavior. For example, heatmaps are used to display the behavior of users on a website through pattern recognition. Particular colors are used to show the website’s frequent and infrequent clicks.

b. Hierarchical Representation of Information

A treemap is another data visualization tool used to showcase the hierarchical representation of information. It includes nested rectangles which represent categories within the area in a “tree”. Individuals can compare and display the hierarchy, quantity, and pattern based on their space on the chart.   

5 Tips on How to Visualize Data to Facilitate Problem-Solving

1. Identify Trends and Patterns

a. Recognizing Correlations in Data

You can visualize data by recognizing their correlations. Once you know their relationship, you get to group them, identify patterns and trends, or display their correlation visually to enable faster communication and more efficient problem-solving.

b. Uncovering Anomalies and Outliers

Anomaly and outlier detection is another power of data visualization. You get to uncover anomalies and outliers that pertain to data that are significantly different from other data. By detecting these, you get to know what area you should focus on during problem-solving.

2. Simplify Complex Information

a. Condensing Large Datasets

Data visualization tools like charts allow you to break down or condense large datasets into smaller and easy-to-understand formats. You categorize data or divide them into groups instead of just seeing them as a whole. By simplifying large amounts of data, it is easier to get insights and show findings you won’t get just by looking at the raw datasets.

b. Enhancing Data Accessibility for Stakeholders

By making complex data easier to understand, you enhance their accessibility for stakeholders. Your stakeholders, whatever their profession or level of expertise, can easily grasp the information you want to deliver because you have made the data accessible enough for them to understand quickly. This allows for more effective communication between you and your stakeholders.

3. Support Hypothesis Testing

a. Visual Confirmation or Refutation of Hypotheses

Data visualization tools enhance hypotheses by confirming, refuting, challenging, or expanding the ideas based on the existing data. Visualization can showcase if the hypotheses are affected by the division of shapes, data outliers, and particular subgroups.

b. Iterative Problem-Solving Processes

You can discover contradictory or unexpected findings to your hypotheses during your iterative problem-solving process through data visualization tools. You test your hypothesis repeatedly until you achieve your desired result. With visualization, you can continually improve your hypothesis.

4. Tools and Software for Visual Problem-Solving

a. ClickUp 

ClickUp is a visual problem-solving software that ensures the efficiency of your team’s collaboration. It makes sure that the members of your team are on the same page all the time. A live collaboration tool lets you create or edit documents with your team. It also includes virtual whiteboards to aid you in your problem-solving processes. The whiteboard feature helps your team brainstorm or generate ideas to solve problems you want to address. Mind maps are also available on the site, which lets you identify significant relations, gather useful insights, and execute creative approaches through mapping the connections between datasets and concepts. 

b. Hive 

Hive is a data visualization tool that provides insightful solutions to your problem-solving processes. With Hive, you can get your team to face difficult challenges and improve their problem-solving abilities. It’s an engaging visual problem-solving platform that makes you see what your team members are doing. Its collaborative work management feature allows you to work with your team to address and solve issues efficiently. It has a user-friendly interface, project hierarchy, Gantt view, and Kanban view, which help you better manage your timelines and progress.

c. Tableau 

Tableau is one of the most popular tools for visual problem-solving big companies use. With the platform, you can make several types of maps, graphs, and charts and connect to various sources of data. It efficiently produces visualizations from small-scale to large-scale datasets commonly used for artificial intelligence and machine learning. Tableau is known for its high-quality performance, user-friendly interface, and great data visualization tools.

d. Trello 

Trello is a visual problem-solving platform that effortlessly streamlines your work processes by organizing tasks using the drag-and-drop feature. Its collaboration and communication tools let you prioritize tasks and responsibilities efficiently. It has a map that exhibits an overview of your projects so you can see tasks, progress, and dependencies visually. Trello ensures you can solve issues through its effective task-management tools

e. Asana

Last on the list is Asana. It is another well-known visual problem-solving platform that improves your decision-making efficiency. It can be used for several small projects and for teams without complicated workflows. It has a customizable workflow feature aids your team in their unique needs by automating repetitive tasks. It has Kanban boards, Gantt charts, and other data visualization tools used to enhance your problem-solving processes. Lastly, it can be integrated with popular tools like Google Drive, Microsoft, Zoom, etc.

5. Integration with Business Intelligence Systems

a. Leveraging Data for Strategic Decision-Making

Data visualization tools help you leverage data to have strategic decision-making. Visualized data integration allows you to access the data you need making it easier for you to make informed decisions for your business, fueling your business’ competitive intelligence.

b.  Real-Time Analytics and Reporting

Real-time analytics and reporting involve gathering live data and sending it to users as it happens. You relay information based on the latest happenings so you or your managers can decide quickly based on the visualized real-time data you reported.

Practical Applications of Visual Problem-Solving

1. Business and Financial Analysis

a. Visualizing Key Performance Indicators (KPIs)

You can use visual problem-solving in a practical setting by using it for your business and financial analysis. You can apply it by visualizing your Key Performance Indicators or KPIs. To do it, you can simply compare a base value to a target value. You can also use charts and graphs to efficiently present the quantitative nature of your KPIs so you can bring out insightful observations from your team. It simplifies evaluating your project or campaign by clearly seeing the KPIs in place.

b. Forecasting and Trend Analysis

Another way to apply visual problem-solving is to forecast and analyze trends. Using visual tools like line and bar graphs, you see trends over time and draw forecasts from them. You see and build a pattern recognition over time, allowing you to create assumptions that you can use in your next problem-solving sessions. With visual tools, you get the idea of what may happen in the future based on your previous reliable visual data.

2. Scientific Research and Exploration

a. Visualizing Experimental Data

Data visualization plays a significant role in scientific research and exploration. It is applied by visualizing experiments to make them easier for stakeholders to understand. Using graphs, charts and other tools allows stakeholders to efficiently determine issues, identify new trends, and gain insightful information from hard-to-grasp experimental or novel data.

b. Communicating Research Findings Effectively

With visual aids, you also get to communicate your research findings effectively. You can use line graphs to see the changes in trends over time. You can use pie charts to show the percentage of an item from a whole and draw conclusions from them. Visual aids make it easier for you to convey the results of your research to other people.

Best Practices for Visual Problem-Solving

1. Choosing Appropriate Visualization Techniques

To maximize the potential of visual problem-solving, you need to pick the appropriate visualization techniques for your team. You have to ensure that the strategy you employ will help you and make your job easier rather than burden you. By choosing the appropriate data visualization techniques, you can efficiently solve even complex problems with your team. You ensure that your team members are knowledgeable of the strategy so you can reap its full potential.

For teams working with structured datasets, WebDataRocks is a free JavaScript pivot table component that simplifies the preparation step before visualization. It lets you group, filter, and aggregate raw data directly in the browser — then pass the result to your preferred charting library. WebDataRocks integrates out of the box with Google Charts, Highcharts, amCharts, and FusionCharts, giving you the flexibility to choose the right chart type for your audience without manual data preprocessing. Explore the available chart integrations here.

2. Ensuring Data Accuracy and Integrity

Whatever industry you are in, you must ensure your data’s accuracy and integrity. You have to make sure that all data you present visually are facts and are based on research. You must have evidence to prove and back them up when questions arise. You must double-check all the data present and be 100% sure that they are accurate. This will allow you to provide real information to your team or stakeholders and prevent you from having problems in the future.

3. Encouraging Iterative Exploration and Analysis

You improve by repeating or doing the same thing daily. The same is true for visual problem-solving. You should employ iterative exploration and analysis to ensure that you are constantly improving your decision-making. By exploring and analyzing things continually, you allow continuous adjustment to your project or business process based on the results and feedback you get.

Conclusion

All in all, data visualization truly helps in the decision-making and problem-solving process. It simplifies data and makes them easier to understand for stakeholders. It also enables more engagement from your team and allows them to gain new insights based on the data they see visually. Visual tools and platforms enhance your and your team’s decision-making abilities, providing you with more insightful solutions to solve problems and challenges.

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Thank you for the article to Vy, she is a content writer for Removal.AI — an AI tool used to remove background from images for social media, eCommerce, web, app development, and marketing automation. She loves to share her insights about tech, AI, and marketing. 

What else to read?

• Data visualization with WebDataRocks & Chart.js: create a dashboard in 5 min
• Navigating the Future of Big Data and Analytics: Trends and Best Practices for 2024
• Tutorial: How to create a dashboard with WebDataRocks and AnyChart
• Top data visualization blogs to follow in 2023
• Pivot Tables: The Ultimate Guide

Recently we’ve talked about the basic principles of data visualization and charts for comparing discrete groups of data. Now it’s time to discover charts for one of the most common types of data analysis – a comparison over time.

Today we’ll dive into a process of choosing the comparison charts that display the data over a period of time and help to track trends in changes for one or multiple categories during that period. All of them prove to be extremely helpful in visualizing the performance with respect to time.

The choice of the comparison chart highly depends on how you treat time – as discrete or continuous data. It’s totally up to whether to use bars to focus on individual dates or lines to observe trends over a continuous interval.

So, let’s start!

Line chart

Line charts (also known as trend lines) are best for illustrating trends and volatility in the data. To draw a line chart, plot the data points on a Cartesian plane and connect them with a line. As a rule, time is plotted on the X-axis (as well as any other independent variables) while the values are plotted on the Y-axis.

Purpose

Use it to depict how the data changes over a continuous time period, either inside one category or among multiple ones.

Recommendations

• If the sum of values is important, consider using an area chart instead of a line chart.
• Keep it neat: don’t plot more than three-four lines per chart.

Examples and variations

• A single-line chart

Using this chart, you can compare a single metric over time.

• A multi-line chart
  

Works best for comparing values of multiple categories or groups to one another over time. To distinguish lines by categories and make the chart as readable as possible, plot lines of distinct colors and widths.

• Dual-axis line chart

It is the same as a multi-line chart but with the second Y-axis being added to it. The several usage scenarios are possible:

• For comparing two data series with the same measure which is expressed using different magnitudes (value ranges).
• For comparing two data series which values are expressed with different units of measurement (e.g., in degrees Celsius and Fahrenheit, in degrees and hours, etc).

Column chart

A column chart consists of vertical bars, the lengths of which are proportional to the values. It’s suitable not only for comparing the data between various categories but also for showing trends for the same or multiple measures over discrete time intervals.

Recommendation

If you have long data labels or/and a large number of data-series, it’s better to use a bar chart instead as it uses space more rationally.

Example

Step chart

A step chart is also known as a step graph. Its main difference from a line chart lies in a method of connecting the points. Instead of connecting the points using the shortest distance between them, it plots vertical and horizontal lines to connect the data points on a plane. Vertical lines can be interpreted as abrupt changes in time-series data.

Purpose

Use it to track the discrete changes in the data that happen at specific moments of time.

Recommendations

• Use it with a timestamp data, or with one that has missing values for some dates.
• To communicate the magnitude of change, fill an area under the line with color.

Example

Area chart

This chart is graphed on the X- and Y-axis, with values being connected using line segments. The area between line segments and axes is filled with color.

Purpose

Use it to accentuate on how the magnitude of the cumulative value evolves over time.

Recommendations

• Assign contrast colors to different data series to help your audience compare values at glance.
• When communicating trends of multiple categories, avoid overlapping plots – this representation may be misleading for your audience. Prefer using stacked area charts instead.
• If the differences between the successive values are tiny, give preference to a line chart.

Examples and variations

• A single-series area chart

• A multi-series area chart

• A stacked area chart
  The elements for data series are stacked at each value and are rescaled to add up to 100%. This extension of the area chart is more readable, isn’t it?

Track a stock’s daily price, including gains and losses within each trading day.

Candlestick chart

A candlestick chart is a specialized way to show price movement over time, most commonly used for stocks, currencies, and other financial instruments. Each “candle” represents a fixed time interval (a day, an hour, a minute) and shows four values at once: the opening price, the closing price, the high, and the low. Candles are usually color-coded: one color when the closing price is higher than the opening price, and another when it’s lower.

Purpose

Use it to track price changes over time and spot trends in market movement. Unlike a line chart, it shows the full price range and whether prices went up or down during each period.

Recommendations

• Best suited for financial and time-series data where the open, high, low, and close all matter, not just the general trend.
• Keep the time interval consistent across all candles (all days, all hours, etc.) 
• For long time ranges with many data points, consider allowing zoom/pan so individual candles remain readable.

Example

Summary

At WebDataRocks, not only we enhance your reporting experience but share knowledge about the best modern visualization techniques.  

Hopefully, this third step of our data visualization journey helps to deepen your knowledge of the charts. We are sure that all of them deserve to take a worthy place in your presentation.

What’s next?

To keep up with all the contributions to our project, check out all the blog posts on the data visualization topic:

• Power of Data Visualization and Charts
• Best Charts to Show Discrete Data
• How to Choose Charts to Show Data Composition
• Best Charts to Show Correlation
• Best Charts to Show Data Distribution

If you’re working with data visualization, you probably have heard about PivotTable.js. It’s a lightweight, open-source JavaScript Pivot Table written by Nicola Kruchten and has been a well-known tool in data visualization for a long time.

You can explore the library yourself on its official GitHub repository, where the full source code is available, or check out a quick demo on YouTube recorded by the author to see how it works in practice.

What makes it stand out is its openness. Because it’s fully open source, you can get free access to the code and full control. It works quite well when you want to build a custom solution from the ground up.

There’s also even a React version called react-pivottable and maintained by the same author, which makes integration into modern apps a bit easier.

At the same time, the library shows its age, as it was built in 2012. It wasn’t updated at all recently, and you can feel that in the overall experience. 

This is where WebDataRocks pivot grid component takes a different direction.

It gives you a complete, polished solution out of the box. The built-in UI is already there; it looks modern, and you can integrate it into your project much faster without building everything from scratch. But even if you want to add more customization, you can also achieve it! 

As examples, we’ve got lots of showcases that demonstrate how WebDataRocks can be used in different ways: from complex analytical dashboards to daily usage habits trackers. 

• HR Analytics with WebDataRocks
• Marketing Campaigns Analysis
• WebDataRocks analytical sport Dashboard
• WebDataRocks Bento Styles Dashboard
• Santa’s Letter Management System

Even just looking at these titles, you can already see how diverse the use cases are and, for sure, it’s not even near the limit of its possibilities.

In reality, it’s a very flexible data visualization component that can be used across many areas and industries, including finance, analytics, audit, logistics, and more. Also, we’ve got some practical examples about how to use it in marketing, sales, industry, and HR analytics.  Some of the more “fun” demos are just there to show how flexible it is and how easily it can work with very different kinds of data.

In short, WebDataRocks works as a universal tool for handling data. You can structure, filter, and display it however you need. That’s why it’s useful both for serious, data-heavy projects and for more creative use cases.

What people say about PivotTable.js and WebDataRocks

PivotTable.js struggles with large datasets, which may be problematic for users. There’s no information about its limits in official docs, but some users on StackOverflow inform that while working with 2000 rows, there’s a slight delay, but with 5000 rows, it completely freezes.

On the other hand, WebDataRocks handles larger datasets (the limit is 1 MB, but it’s actually enough for most use cases) more comfortably and continues to receive updates, which makes it feel better aligned with current development needs. Also, WebDataRocks appears in numerous Reddit discussions. You may notice that people there describe it as a feature-rich component.

Flexmonster as an alternative to PivotTable.js & WebDataRocks

Even though WebDataRocks isn’t open source, it’s still completely free. Unlike many similar tools that are limited to non-profit or personal use, you’re welcome to use it in commercial projects and real business applications as well. And if you ever need something more advanced, there’s also Flexmonster – a commercial product created by the same team. It’s designed to handle even larger datasets, advanced analytics, and enterprise-level requirements.

For example, it has better performance with really large datasets, including built-in support for server-side processing. This means you can work with millions of records without loading everything into the browser, which isn’t really possible with lighter tools.

Another big advantage is the wide range of integrations. Flexmonster allows connecting to more advanced data sources, such as backend databases, and supports server-side technologies. 

Also, it supports many popular frameworks such as React, Angular, and Vue.js (which are also supported by WebDataRocks), but it doesn’t stop there. It also works with less common or newer tools like Svelte, Flutter, Blazor, Nuxt, Next.js, and much more. This makes it a very flexible choice for real-world applications.

Wrap-up

In conclusion, it really depends on what you really need. PivotTable.js is great when you value openness and want full control, especially for smaller projects. But when the goal is to move faster, work with more data, and get a clean interface without extra effort, WebDataRocks feels like a better choice.

That’s why it works so well as an alternative – not because one is completely better than another, but because they’re designed for slightly different tasks.

We are sure you’ve received many surveys on various topics and have been filling them out. But have you ever thought about how to better store this info in one place so it can be easily analyzed and navigated in the future? For sure, we’ve got a solution for you, and it’s WebDataRocks Pivot Grid!

As you know, better to learn anything by example. So, we’d love to present our new Medical Survey demo to you and walk you through the process of building it. Let’s start!

Step 1: Adding WebDataRocks Pivot Table to the Page

First step is easy as always: just add WebDataRocks to your project. It’s possible to achieve it with this code snippet:

<link href="https://cdn.webdatarocks.com/latest/theme/default/webdatarocks.min.css" rel="stylesheet" />
<script src="https://cdn.webdatarocks.com/latest/webdatarocks.toolbar.min.js"></script>
<script src="https://cdn.webdatarocks.com/latest/webdatarocks.js"></script>

Step 2: Building the Patients’ Experience Report’s Structure

Next, we create a pivot table where each row represents a patient visit, and each column reflects hospital performance metrics. In other words, just building the structure of our dashboard: include which rows, columns, and measures we’d like to display: 

const pivot = new WebDataRocks({
  container: "#pivotContainer",
  width: "100%",
  report: {
    dataSource: {
      data: getData()
    },
    slice: {
      rows: [
        { uniqueName: "Name" },
        { uniqueName: "Surname" },
        { uniqueName: "Department" }
      ],
      columns: [{ uniqueName: "Measures" }],
      measures: [
        { uniqueName: "StaffFriendly" },
        { uniqueName: "WouldRecommend" },
        { uniqueName: "VisitSuccessful" },
        { uniqueName: "FacilityClean" }
      ]
    },
    options: {
      grid: {
        type: "flat",
        showGrandTotals: false
      }
    }
  }
});

Step 3: Preparing and Preprocessing the Info for Better Data Analysis

Okay, now we need to make sure our data is ready for analysis. In our case, the survey contains many Boolean values (true/false), which are not ideal for aggregation.

To solve this, we preprocess the data by converting all Boolean values into numeric ones (1 and 0). This allows WebDataRocks to correctly calculate totals and, in the future, implement a wide variety of aggregation functions.

function preprocessData(data) {
	return data.map(function (record) {
		var result = {};
		for (var key in record) {
			result[key] =
				typeof record[key] === "boolean" ? (record[key] ? 1 : 0) : record[key];
		}
		return result;
	});
}

Now, instead of using raw data, we first transform it and then pass it to the pivot table:

dataSource: {
	data: preprocessData(getData())
}

Step 4: From Raw Data to Visual Feedback with Data Visualization Techniques

The thing is, in our survey are too many true/false answers (which are now converted into 1/0 numeric values), and they are not really readable in this format. Let’s transform them into visual signals!

And at the beginning, we just list columns that contain these boolean-type answers:

const fieldsToFormat = [
  "StaffFriendly",
  "WouldRecommend",
  "VisitSuccessful",
  "FacilityClean"
];

But the question is, in which format is better to represent them then?

Why should we even choose the one? We can apply a few of them! So, the user can pick the option that’s most convenient: pills, emojis, or colored dots.

let currentMode = "pills";

function customizeCellFunction(cell, data) {
  if (
    data &&
    data.type === "value" &&
    fieldsToFormat.includes(data.hierarchy.uniqueName)
  ) {
    if (currentMode === "pills") {
      cell.text = data.value == 1
        ? `<span class="pill yes">Yes</span>`
        : `<span class="pill no">No</span>`;
    }

    if (currentMode === "emojis") {
      cell.text = data.value == 1 ? "✅" : "❌";
    }

    if (currentMode === "dots") {
      cell.text = data.value == 1
        ? `<span class="dot yes"></span>`
        : `<span class="dot no"></span>`;
    }
  }
}

Step 5: Switching How Insights Are Displayed in an Interactive Report

Here’s the time to do switching button for it. So, just add a simple dropdown so users can change the visualization style:

<select onchange="changeDisplayMode(this.value)">
  <option value="pills">Pills</option>
  <option value="emojis">Emojis</option>
  <option value="dots">Colored Dots</option>
</select>

And, for sure, connect it:

function changeDisplayMode(mode) {
  currentMode = mode;
  pivot.customizeCell(customizeCellFunction);
}

Now it feels like completely different dashboards depending on the view:

Step 6: Styling Web Pivot Table

And now, let’s bring real medical style to our dashboard so everyone understands what the topic is at first glance!

With WebDataRocks, you don’t need to create the whole design from scratch. It has 8 already built-in themes, and look, there’s a light-blue one! I guess it’s a perfect match for our dashboard. To implement it, just add this code snippet to your project:

<link href="https://cdn.webdatarocks.com/latest/theme/lightblue/webdatarocks.min.css" rel="stylesheet" />

But don’t be scared, we don’t limit our users to just built-in themes, you can also create your own custom component theme! 

Still not enough styling? We’ve got another idea. Let’s add a header to make our dashboard even more recognizable.

<header class="page-header">
  <div class="header-inner">

    <div class="header-logo">

      <svg width="32" height="32" viewBox="0 0 32 32">
        <rect width="32" height="32" rx="8" fill="#1A6BAA" />
        <rect x="13" y="6" width="6" height="20" rx="2" fill="white" />
        <rect x="6" y="13" width="20" height="6" rx="2" fill="white" />
      </svg>
      <span class="header-brand">MediTrack</span>
    </div>

    <div class="header-title">
      <h1>Patient Visit Report</h1>
      <p>Hospital Quality &amp; Satisfaction Overview</p>
    </div>

  </div>
</header>

And for sure, don’t forget to style it properly. You can see how it all comes together in the demo.

So here it is: our dashboard is ready to help hospitals track patient experience! Hope this demo helped you learn new features of WebDataRocks and gave you ideas for implementing them in your real projects. 

By the way, #CodePenChallenge inspired the idea for this dashboard. And being honest, it’s not our first time experimenting with it. If you want to see more creative projects, we recommend reading our articles on dev.to about it: 

• #CodePenChallenge Halloween Time: Building a Pivot Table with Horror Movies using WebDataRocks
• Building a Snowy Weather Dashboard with Free Pivot Grid Library
• #CodePenChallange: Colorful Way of Data Presentation Using Data Viz Library

Recently we discussed which charts are best suited for comparison over time.

Today we’ll shed light upon the main ideas behind a data composition.

The idea of this kind of visualization lies in helping understand how individual parts comprise a whole. With data composition, it’s easy to focus attention on the importance of each part with regard to the total value.

Though most charts show the relative value, you can use some of them to compare absolute values.

Let’s move on to the charts that are best for a part-to-whole analysis.

All these charts can be divided into two categories:

• Charts that show changes in composition over time. In case you have a few periods, it’s preferred to use stacked bar or column charts. If you have a lot of time spans, give your preference to stacked area charts.
• Charts that show the static composition of the data. These include pie, waterfall charts, and treemaps.

Composition over time

Stacked area chart

A stacked area chart works the same way as an area chart, but allows comparing multiple data series by placing values of groups on top of each other. It works best if you want to show relative and absolute differences between categories.

Purpose

Use it for tracking changes of a total value across different categories over a period of time.

Recommendations

• Use the data only with positive values.
• It’s better not to use too many groups so as to avoid overlapping of plots.

Example

Measure the company’s performance over the years:

Compare with a version with overlapped plots:

The second one is neater and easier to read, isn’t it?

Stacked bar chart

You may have used a bar chart for comparing the changes in values over time or across the categories, but for displaying the size differences for parts of a whole, it’s better to take advantage of stacked bar charts. In this chart, the bars are placed on top of each other.

Purpose

Use it to show how a larger category is divided into subcategories and how each subcategory contributes to the total amount. If you need to show the percentage for each value, use a 100% stacked bar graph instead.

Recommendations

• It’s better not to use too many segments for each bar as the graph may become difficult to read and interpret.

Example

Over time, comparison of sales of books divided by genres:

Static composition

Pie chart

A pie chart is one of the most widely used types of visualization. The first mention of the pie chart is attributed to William Playfair in 1801. Though many people criticize pie charts, they play an important role when it comes to understanding the parts of the whole.

Purpose

Use it to show proportions or percentages of categories in the form of a circle divided into segments. The value of each category is proportional to the corresponding length of the arc.     

Recommendations

To make the chart comprehensible, follow these basic rules:

• It’s better not to try visualizing large amounts of data – choose up to 6 categories to make a pie chart easy to interpret.
• Make it readable – label segments outside the chart.
• Make sure your data fits this type of chart. The data should be discrete. Otherwise, incorrect use of the data may lead to misleading visualization.
• Avoid using the 3D version of the pie chart – it may distort true proportions between categories.

Example

Visualizing the categories that bring the most traffic:

Doughnut (ring) chart

A doughnut chart is a variation of the pie chart with a round hole in the center. It has the same purpose as a pie chart does, but its special feature is the capability to contain supplementary information, which can improve the readability of the chart.

Example

Shares of sales by countries:

Treemap

A treemap chart looks unpretentious, yet it’s powerful. Thanks to their compactness, treemaps help to illustrate a large amount of data in a hierarchical view.

Purpose

Use it to visualize the hierarchical data in a form of clustered rectangles which together represent the whole. Each node (or group) is represented by a rectangle, which area is proportional to the value. Colors help to add dimensions such as groups and subgroups.

Recommendations

• Add some interactivity to the chart to achieve a detailed view of the data and be able to drill through the hierarchies (e.g., from continents to regions and countries).
• To avoid misinterpretation of your data composition analysis, it’s better not to use many parts so as not to confuse the audience.

Example

This treemap shows the number of the world population. You can drill through to know the levels of a population of each hierarchy and subhierarchy.

Funnel (pipeline) chart

A funnel chart is a type of chart that depicts the decrease at each stage in a process. In other words, it illustrates how many items are lost at each stage of a particular process. This is why it’s widely used in marketing and sales. The most popular use case is the analysis of conversions.

If there’s an increase at each stage, a funnel chart turns into a pyramid chart.

Purpose

Use it to identify bottlenecks in a workflow by ordering the entire process by stages.

Example

Conversion of website users into customers:

Waterfall chart

A waterfall chart shows how the starting value is increased or decreased by a sequence of intermediate values that lead to the total value. Such a process is called cumulative. This type of chart is commonly used in the finance domain.

Purpose

Use it to understand how the value is gradually changed over time or across categories.

Example

Final words

These visualization techniques can be effectively combined with WebDataRocks for interactive pivot table analysis and multidimensional reporting.

We do hope you enjoyed this overview of charts and recommendations. Now you are ready to pick the right type of visualization to make your data analysis be ahead of the curve.

Thank you for reading!

What’s next?

• Power of Data Visualization and Charts
• Best Charts to Show Discrete Data
• Charts for Comparison Over Time
• Best Charts to Show Correlation
• Best Charts to Show Data Distribution

In the final part of the data visualization project, we’ll discuss the charts that visualize the distribution of univariate and bivariate data.

Histogram

A histogram is the most commonly used plot type for visualizing distribution. It shows the frequency of values in data by grouping it into equal-sized intervals or classes (so-called bins). In such a way, it gives you an idea about the approximate probability distribution of your quantitative data.

Structure

The histogram is composed of vertical or horizontal bars. The height of each bar corresponds to the frequency of values that fall into this bin. By changing the bin width, you also change the number of bins – this will affect the shape of a distribution.

Purpose

To visually represent the distribution of univariate data. Additionally, with the histogram, you can figure out information about the center, spread, and skewness of data, as well as the extreme values, missing or non-typical values (outliers). In addition, you can check whether the data has multiple modes. 

One should not confuse histograms with bar or column charts – though these graphs are alike, they play totally different roles in data visualization:

• The histogram illustrates the frequency of continuous values that are grouped into ranges of a data series and represents distribution while the column chart compares values of a categorical data.
• The most noticeable visual difference is in the existence of spaces between bars: there are no spaces between bars in the histogram but they can be in the column/bar chart.
• It’s impossible to rearrange the bars in the histogram. With the column chart, it can be done without the loss of meaning.
• Columns in the column chart have equal widths but columns in the histogram – don’t. 

Example

The distribution of the country’s population:

Box and Whisker Plot

A box and whisker plot is one of the most popular charts when it comes to statistical analysis of data distribution. 

Structure

A box contains three important numbers: the first quartile, median, and third quartile. The other two numbers are the minimum and maximum – these are represented by whiskers.

These five numbers divide the dataset into sections. Each section contains around 25% of the data.

Example

Conclusion

Today you’ve learned more about charts that can be used for visualizing data distribution. We encourage you to learn by doing and try creating such charts in your data analysis project.

Analyzing data distribution with pivot tables

A useful starting point for distribution analysis is summarizing your raw data into frequency groups. With WebDataRocks, you can quickly aggregate datasets and export the results to build histograms or box plots in your preferred charting library. Check out the WebDataRocks + Google Charts integration for a practical example.

What’s next?

Eager to learn about other chart types? You are welcome to read the previous blog posts of the data visualization project:

• Power of Data Visualization and Charts
• Best Charts to Show Discrete Data
• Charts for Comparison Over Time
• How to Choose Charts to Show Data Composition
• Best Charts to Show Correlation

References

• Histogram in Google Charts
• Box Plots in plotly.js

The right choice of web reporting tool can be a crucial factor for any data-driven business. Software development is a truly complex process that requires managing and coordinating many different modules and components for a reporting solution to work flawlessly.  So in this article, we decided to dive into the exciting world of JavaScript libraries and components for report applications, web reporting tools, or software with reporting elements and created a collection of the best JavaScript libraries for report applications.

(more…)

Here, we will review discrete data graphs that help find similarities and differences between various categories of data and discuss their purposes and specifics.

But firstly, let’s sort out what a discrete data is.

What is discrete data?

Data is discrete if you can answer affirmatively on the following questions about it:

• Is it countable?
• Is it possible to divide the data into smaller parts? (i.e., to categorize it)

Discrete data can contain only a finite number of values. One of its notable properties is that, unlike continuous data, it can’t be measured, only counted.

Examples of discrete data: the number of players in a team, the number of planets in the Solar System.
Examples of non-discrete (continuous) data: height, weight, length, income, temperature.

The following charts work especially well for representing the discrete data:

• Bar chart
• Stacked bar chart
• Column chart
• Stacked column chart
• Spider chart

Bar chart

The simplest and the most popular type of chart to show discrete data. It displays grouped data using rectangular bars with lengths that are proportional to the values. Bar charts are broadly used in marketing and finance.

Purpose

Use it to compare data points that are spread across categories with each other.

Pros

Makes data more readable if the categories have long names or if you have more than 10 categories.

Recommendation

Arrange bars on the chart in a logical ordering: ascending or descending one.

Examples and variations

• A single-series chart in which the values are compared inside one category
  

• This is an example of a clustered (grouped) bar chart. Use it for comparing multiple series inside the same category
  

Stacked bar chart

A stacked bar chart is composed of multiple bar series stacked horizontally one after the other. This modification of the bar chart makes it easier to track the variation in the individual values and their total value.

Purpose

Use it if you are interested in the proportional contributions either of categories to the total or of values within each category.

Pros

If designed wisely, this type of chart is the best option for comparing multiple categories by measuring the bar lengths.

Recommendation

Limit the chart to six-seven series, otherwise, a chart will be cluttered and difficult to interpret.

Example

Column chart

It’s constructed the same way as a bar chart but with a vertical axis for categories and a horizontal – for their values.

Purpose

It helps track changes in values over time by comparing total column lengths.

Pros

Highly readable when comparing values between a small number of categories (less than 12).

Recommendation

In case your dataset has negative values, use the column chart instead of the bar chart as the negative values are associated with a downward direction.

Examples and variations

• A single-series column chart:

• Clustered (grouped) column chart:

Stacked column chart

A stacked column chart is composed of multiple column data series stacked on top of one another.

Purpose

The stacked column chart is designed to compare totals and notice changes at the item level that are likely to have the most impact on changes in totals.

Pros

• Best for part-to-whole comparisons over time or across categories
• It helps to get an understanding of the integral picture in a quick glance without a focus on the details

Examples

Bullet chart

A bullet chart is a modification of a bar chart adapted by modern business needs. It can be displayed both vertical and horizontal. The chart consists of a target marker that represents the target value, an achievement bar that represents the current value of a metric and a comparison range.

Purpose

It was designed by Stephen Few for tracking progress toward a goal and measuring how far you are from the target.

Pros

It removes the necessity to use circular/linear gauges and meters on a dashboard.

Recommendation

Use custom background colors to encode the interpretation of values that lie within the comparison range.

Example

Usual meaning: the lighter the background, the better the result. However, in the case of “Expenses”, according to its negative sense, you can reverse the quantitative scale.

Spider chart

In this chart, each variable’s axis starts from the center point. Axes are arranged radially around it. The value is presented by an anchor on the axis. This data point is connected with the axis by a line. It’s the process of plotting a polygon. As you may notice, it reminds a spider web; hence the name. Equivalent names are ‘polar chart’, ‘web chat’, ‘radar chart’, and ‘star plot’.

Purpose

It’s designed for comparing multivariate data that has three or more quantitative variables.

Pros

Works best for comparing products by highlighting their strengths (features) and weaknesses.

Recommendations

• Use color coding and labels to distinguish multiple items when comparing them.
• Don’t be misled by the area of the polygons: it is increased by the squares of values. Thus, one may think that tiny changes are more important than they really are.
• Take into account that the area and the shape of the polygons may vary significantly depending on the position of axes.

Examples

Summary

All of these chart types can be integrated with WebDataRocks to create interactive reports, dashboards, and pivot table visualizations for business analytics.

We truly hope this overview will help you decide which charts are best suited to your data and give you a general idea of how to compare discrete groups graphically.

Stay tuned for further updates!

What’s next?

• Power of Data Visualization and Charts
• Charts for Comparison Over Time
• How to Choose Charts to Show Data Composition
• Best Charts to Show Correlation
• Best Charts to Show Data Distribution
• How to use WebDataRocks Pivot Table with Google Charts

Demos

• Column chart
• Bar chart
• Stacked column chart
• Stacked bar chart

Have you already received gifts from your dearest ones? So, guess what? WebDataRocks prepared a present for you, too. We’ve mentioned already that our pivot table can be used by anyone, but have you ever thought that even Santa can use WebDataRocks? 

We think that our pivot table can be a really convenient way to store and process all the children’s letters. 

Didn’t expect that? However, today we want to show you how to do it yourself. 

Step 1: Adding WebDataRocks to the Page

First, let’s include WebDataRocks styles and scripts in our HTML:

<link href="https://cdn.webdatarocks.com/latest/theme/stripedteal/webdatarocks.min.css" rel="stylesheet" />
<script src="https://cdn.webdatarocks.com/latest/webdatarocks.toolbar.min.js"></script>
<script src="https://cdn.webdatarocks.com/latest/webdatarocks.js"></script>

Step 2: Initializing the Pivot Table

Now, let’s bring the pivot table to life, where each letter will be shown as a single row. Probably, it will be quite convenient for Santa to ensure that no present for a child is forgotten.

var pivot = new WebDataRocks({
  container: "wdr-component",
  customizeCell: customizeCellFunction,
  width: "100%",
  height: "100%",
  report: {
    dataSource: {
      type: "json",
      data: getData()
    },
    slice: {
      rows: [
        { uniqueName: "name" },
        { uniqueName: "city" },
        { uniqueName: "date" },
        { uniqueName: "gifts" },
        { uniqueName: "letter" }
      ]
    },
    options: {
      grid: {
        type: "flat",
        showTotals: "off",
        showGrandTotals: "off"
      },
      datePattern: "MMMM d, yyyy",
      showAggregationLabels: false
    }
  }
});

Step 3: Replacing Long Letters with a Button

But here’s a tiny problem: in our dataset, the text of letters is too long to display, so let’s replace it with a button, clicking on which we will see the whole text in a convenient format. To achieve it, just add a customizeCellFunction:

function customizeCellFunction(cell, data) {
  if (
    data &&
    data.type === "value" &&
    data.hierarchy &&
    data.hierarchy.uniqueName === "letter"
  ) {
    const escapedLabel = data.label
      .replace(/\n/g, "\\n")
      .replace(/'/g, "\\'")
      .replace(/"/g, '\\"');

    cell.addClass("letter-cell");
    cell.text = `
      <button class="letter-btn" onclick="openLetter('${escapedLabel}')">
        View Letter
      </button>
    `;
  }
}

And add this function to customizeCell in our pivot table component:

var pivot = new WebDataRocks({
  container: "wdr-component",
  customizeCell: customizeCellFunction,
…
)}

And another functions for opening our letter in a new window and closing it: 

function openLetter(letterContent) {
  const unescapedContent = letterContent
    .replace(/\\n/g, "\n")
    .replace(/\\'/g, "'")
    .replace(/\\"/g, '"');

  document.getElementById("letterContent").textContent = unescapedContent;
  document.getElementById("letterModal").style.display = "block";
}

function closeLetter() {
  document.getElementById("letterModal").style.display = "none";
}

Step 4: Styling

Now, let’s create an actual Christmas atmosphere magic using customization.

Step 4.1: Choosing a Theme for the Pivot Table

Before creating custom styling, we’ll apply the built-in Striped-Teal theme:

<link href="https://cdn.webdatarocks.com/latest/theme/stripedteal/webdatarocks.min.css" rel="stylesheet" />

Moreover, you can choose from our eight built-in themes to quickly change the appearance of the pivot table.

Step 4.2: Styling the Letter Pop-up

Next, let’s focus on how the letter itself is displayed in a pop-up window: 

#letterModal {
  display: none;
  position: fixed;
  inset: 0;
  background-color: rgba(0, 0, 0, 0.5);
  z-index: 999;
}

#letterModalContent {
  background: #ffffff;
  max-width: 600px;
  margin: 10% auto;
  padding: 24px;
  border-radius: 12px;
  box-shadow: 0 10px 30px rgba(0, 0, 0, 0.3);
}

#letterContent {
  white-space: pre-wrap;
  line-height: 1.5;
}

Step 4.3: Styling the Background of The Dashboard

As a final touch, let’s add even more holiday vibes in our background by adding a soft gradient with some present emojis:

body {
  background: linear-gradient(135deg, #0f4c75, #3282b8, #0f4c75);
  background-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='40' height='40' viewBox='0 0 40 40'%3E%3Ctext x='20' y='25' font-size='20' text-anchor='middle' opacity='0.1'%3E🎁%3C/text%3E%3C/svg%3E");
  background-repeat: repeat;
  background-size: 40px 40px;
  background-attachment: fixed;
  font-family: "Segoe UI", Tahoma, Geneva, Verdana, sans-serif;
  margin: 0;
  padding: 20px;
  min-height: 100vh;
}

And that was it, our winter festive demo is ready for Santa’s use to ensure that all presents will be delivered this year. Hopefully, this example gives you a few ideas on how you can experiment with WebDataRocks in your own projects, no matter how festive they are.

Okay, we’re being honest — we love all our demos! But we did put together a list of the Top 6 for you today. They’re a perfect way to explore our features, discover how they can be used in different ways, and experience the magic of customization.

Let’s take a look at them together!

Stranger Things Episode Rating

Probably the best example to demonstrate that your pivot table can be customized precisely as you want. Here, we’ve used everyone’s familiar series style, “Stranger Things”. And not only colors and fonts, but we went much further by creating an “upside-down world” effect. You can enter this mysterious and spooky world by simply clicking one button: the demo changes completely and appears with a spotlight effect.

By the way, in this demo specifically for the pivot table, we’ve used our predefined Striped-Teal theme (we have seven more). It can be achieved by easily adding this code snippet:

<link id="wdr-theme" href="https://cdn.webdatarocks.com/latest/theme/stripedteal/webdatarocks.min.css" rel="stylesheet" />

Nutrition Facts for McDonald’s Menu

Okay, but let’s come back to reality. Our next demo is about the worldwide popular fast-food restaurant McDonald’s, and more specifically, the nutritional values of its menu items. And now it is full of bright colors, which we all associate with this cafe. To make it even more obvious, we added burger emojis in the background. Must admit, looks quite tasty.

body {
	font-family: "Quicksand", serif;
	height: 100vh;
	margin: 20px;
	background: #ffd700;
	display: flex;
	flex-direction: column;
	align-items: center;
	background-image: url("data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' width='32' height='32'><text x='0' y='24' font-size='24'>🍔</text></svg>");
	background-repeat: repeat;
	background-size: 64px 64px;
}

Christmas Sales

The Christmas atmosphere is already in the air. Have you prepared presents already? What about packing cells of your pivot as Christmas gifts? Easy to achieve with WebDataRocks! Just a few lines of code, and your dashboard is also ready for the winter holidays.

pivot.on("cellclick", (cell) => {
  if (cell.columnIndex === 3) {
    // Update visibleNumber only for column 3
    visibleNumber.rowIndex = cell.rowIndex;
    visibleNumber.columnIndex = cell.columnIndex;
    pivot.refresh(); // Refresh to apply customization
  }
});

Pumpkin WebDataRocks and Highcharts

As you guessed, our customization can go much further; for example, you can style the Toolbar! Add/delete buttons, change icons, or do whatever you want. In our pumpkin demo, you can notice spooky buttons. To apply it to your own project, just write these code snippet:

function customizeToolbar(toolbar) {
	var tabs = toolbar.getTabs();
	toolbar.getTabs = function () {
		for (let i = 0; i < tabs.length; i++) {
			switch (i % 4) {
				case 0:
					tabs[
						i
					].icon = `<img width=30 height=30 src="https://cdn-icons-png.flaticon.com/512/6433/6433146.png"/>`;
					break;
				case 1:
					tabs[
						i
					].icon = `<img width=30 height=30 src="https://cdn-icons-png.flaticon.com/512/3330/3330533.png "/>`;
					break;
...
			}
		}

		return tabs;
	};
}

But not only is the Toolbar special in this demo. We want to remind you that WebDataRocks can integrate with many charting libraries, and here you can see an example of its interaction with Highcharts

function createAreaChart() {
	pivot.highcharts.getData(
		{
			type: "area",
			slice: {
				rows: [
					{
						uniqueName: "Date.Month"
					}
				],
				columns: [
					{
						uniqueName: "Measures"
					}
				],
				measures: [
					{
						uniqueName: "Price",
						aggregation: "average"
					}
				]
			}
		},
		drawAreaChart,
		drawAreaChart
	);
}

WebDataRocks Pivot Table with Google Charts Map

So, let’s continue the topic of integrations with charts. This demo perfectly demonstrates how a wide diversity of charts can be applied to your data visualization. Your structured table data can be displayed even as a world map with Google Charts Map!

WebDataRocks integrates with Google Charts through a connector that sends aggregated pivot data directly into any chart you choose. Once both components are loaded, the pivot prepares the data, the connector formats it, and Google Charts instantly turns it into a visual. All you really do is place both components on the page, load the connector, and call a function to draw the chart.

<script src="https://cdn.webdatarocks.com/latest/webdatarocks.googlecharts.js"></script>
<script src="https://www.gstatic.com/charts/loader.js"></script>

And more about any chart integrations with WebDataRocks, you can read in our “Available Tutorials” page.

Analytical Sport Dashboard: Y2K Style

Not only for serious work projects, but WebDataRocks can also be used for personal purposes. Plan your daily routine, track habits, manage budgets, or organize any kind of personal data with the same clarity you use at work. This Analytical Sport Dashboard demo perfectly shows how flexible and insightful your personal analytics can become.

These demos are just a glimpse of what’s possible. WebDataRocks can become anything: from a spooky scene to a tasty fast-food board or even a Christmas gift grid. Try out these examples and have fun creating your own unique dashboards!

What else to read?

• Trick or Pivot! Interactive Demos on this Halloween
• Turn Your Data Into a Halloween Treat with WebDataRocks

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